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Recent Advances in Computational Intelligence in Defense and Security pp 635–661Cite as

Evolving Narrations of Strategic Defence and Security Scenarios for Computational Scenario Planning

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 621))

Abstract

Defence and security organisations rely on the use of scenarios for a wide range of activities; from strategic and contingency planning to training and experimentation exercises. In the grand strategic space, scenarios normally take the form of linguistic stories, whereby a picture of a context is painted using storytelling principles. The manner in which these stories are narrated can paint different mental models in planners’ minds and open opportunities for the realisation of different contextualisations and initialisations of these stories. In this chapter, we review some scenario design methods in the defence and security domain. We then illustrate how evolutionary computation techniques can be used to evolve different narrations of a strategic story. First, we present a simple representation of a story that allows evolution to operate on it in a simple manner. However, the simplicity of the representation comes with the cost of designing a set of linguistic constraints and transformations to guarantee that any random chromosome can get transformed into a unique coherent and causally consistent story. Second, we demonstrate that the representation being utilised in this approach can simultaneously serve as the basis to form a strategic story as well as the basis to design simulation models to evaluate these stories. This flexibility fulfils a large gap in current scenario planning methodologies, whereby the strategic scenario is represented in the form of a linguistic story, while the evaluation of that scenario is completely left for the human to subjectively decide on it.

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Notes

  1. 1.

    In this chapter, the implicit Space parameter of an event is not added because it involves a concrete thing—a participant including a Character or an Object—in the story, while the participants of events are left untouched in a generated narration.

  2. 2.

    A flashback is a story played backward. It can be a full flashback in which the whole story is played backward, or a partial flashback in which a subset of events are played backward, with the rest of the events played forward.

  3. 3.

    Using pure random generation, slim chances (\(\frac{4}{7! \times 6!}\)) can be expected to obtain a narration that possesses comparatively high values of consistT and consistS at the same time.

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Acknowledgments

This work was supported by the Scientific Research Starting Foundation for Returned Overseas Chinese Scholars, the Shandong Postdoctoral Innovation Foundation under Grant 140880, and the Chinese Fundamental Research Funds for the Central Universities under Grant 201513001.

Author information

Authors and Affiliations

  1. School of Engineering, Ocean University of China, Qingdao, China

    Kun Wang

  2. Faculty of Arts and Design, University of Canberra, Canberra, Australia

    Eleni Petraki

  3. School of Engineering and IT, University of New South Wales, ADFA, Sydney, Australia

    Hussein Abbass

Authors
  1. Kun Wang
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  2. Eleni Petraki
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  3. Hussein Abbass
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Corresponding author

Correspondence to Kun Wang .

Editor information

Editors and Affiliations

  1. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rami Abielmona

  2. Larus Technologies Corporation, Ottawa, Ontario, Canada

    Rafael Falcon

  3. Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada

    Nur Zincir-Heywood

  4. School of Engineering and Information Technology, University of New South Wales, Canberra, Aust Capital Terr, Australia

    Hussein A. Abbass

Appendix A: Event Recognition

Appendix A: Event Recognition

The following grammatical components in a clause are identified as events.

(E1) VERB without predicative complement: all “tensed or untensed verbs” [52] and idiomatic expression (e.g., show up), prepositional verb (e.g., depend on) and phrasal-prepositional verb (e.g., catch up with) [51].

(E2) Predicative complement with NP as the head: predicative complement whose head is a noun phrase (NP) and the verb belongs to one of the following classes: copulative predicates (e.g., to be, seem), inchoative predicates (e.g., become), aspectual predicates (e.g., begin, continue, end, finish), change of state predicates (e.g., retire, appoint, elect, resign), predicates of evaluation and description (e.g., consider, describe, depict, evaluate) [52]. For instance, “became a good guy” in “He became a good guy.” or “was elected president of the country” in “She was elected president of the country.”

(E3) Predicative complement with ADJECTIVE as the head: predicative complement whose head is an adjective and the verb belongs to one of the following classes: the above-mentioned copulative predicates, inchoative predicates, aspectual predicates, change of state predicates and predicates of evaluation and description, as well as causative predicates (e.g., cause, make) and predicates of perception (e.g., look, hear) [52]. For instance, “looks nice” in “The dress looks nice.” or “made happy” in “The cake made the boy happy.”

(E4) Predicative complement with PP as the head: predicative complement whose head is a prepositional phrase (PP) and the verb belongs to the classes that belong to copulative predicates, inchoative predicates, aspectual predicates, change of state predicates and predicates of evaluation and description, causative predicates and predicates of perception as has been listed in TimeML event annotation standard [52]. For instance, “is in good mood” in “She is in good mood.”, “was behind the tree” in “Cinderella was behind the tree”.

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Wang, K., Petraki, E., Abbass, H. (2016). Evolving Narrations of Strategic Defence and Security Scenarios for Computational Scenario Planning. In: Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol 621. Springer, Cham. https://doi.org/10.1007/978-3-319-26450-9_23

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